Potential application of spectral indices for olive water status assessment in (semi‐)arid regions: A case study in Khuzestan Province, Iran

Abstract Spectral indices can be used as fast and non‐destructive indicators of plant water status or stress. It is the objective of the present study to evaluate the feasibility of using several spectral indices including water index (WI) and normalized spectral water indices 1–5 (NWI 1–5) to estimate water status in olive trees in arid regions in Iran. The experimental treatments involved two olive cultivars (Koroneiki and T2) and four irrigation regimes (irrigated with 100%, 85%, 70%, and 55% estimated crop evapotranspiration [ETc]). The results obtained showed that olive trees subjected to the different irrigation regimes of 85%, 70%, and 55% ETc experienced soil water content (SWC) deficits by 4.5%, 12%, and 20.5% that of the control, respectively. Significant differences were observed among the treatments with respect to measured relative water content (RWC), SWC, and the spectral indices of WI and NWI 1–5. The normalized spectral indices combining NIR and NIR wavelengths were found more effective in tracking changes in RWC and SWC than those that combine NIR and VIS or VIS and VIS wavelengths, respectively. Spectral indices were closely and significantly associated with RWC (.63**<R2<.77**) and SWC (.51**<R2<.67**). Among all the spectral indices investigated, NWI‐2 showed the least consistent associations with RWC (ranging from 4–15% lower than the other indices examined) and SWC (ranging from 1–23% lower than the others). Based on the pooled data on spectral indices, RWC, and SWC collected during the study period, WI, NWI‐1, NWI‐4, and NWI‐5 showed stronger correlations with RWC and SWC than did NWI‐3 and NWI‐2. In conclusion, the spectral indices of WI and NWI 1–5 measured at the leaf level are found useful as fast and non‐destructive estimators of plant water stress in arid regions.


| INTRODUCTION
The olive tree (Olea europaea L.) is a perennial evergreen tree native to the Mediterranean regions (Arenas-Castro et al., 2020) with a high degree of resistance to aridity (Connor & Fereres, 2005). It is a most important tree crop worldwide in terms of the area under cultivation amounting to almost 11 million hectares (Garcia-Tejero et al., 2017).
While oil extraction accounts for about 90% of the world's olive production, the remaining 10% is used as table olive (FAOSTAT, 2018).
The growing concern for human longevity and health alongside the rising public awareness of olive oil's nutritional value has increased its consumption so that the area under its cultivation has witnessed a rapid growth over the past few decades (IOC, 2018) and its production and consumption will expectedly continue to rise in future (Fabbri, 2004). Being no exception, Iran has also witnessed a growth in both area and geographic distribution of olive cultivation.
It is common knowledge that irrigated agriculture is facing a rising challenge in terms of increasing water deficit and uncertainty in water supplies not only because of the prolonged droughts and the global climate change but also due to the escalating competition among environmental, domestic, and industrial water demands (DeJonge et al., 2015). These considerations motivate maximum water productivity in all agricultural activities including olive production.
One option to consider in this situation is deficit irrigation whereby irrigation demand is reduced and crop water use efficiency is enhanced (Mairech et al., 2020). It has also been indicated that optimization of olive oil quantity and quality requires finely tuned water management as increased irrigation, up to a certain level, might result in higher yields but a certain degree of stress is concurrently known to improve oil quality (Ben-Gal, Berenguer et al., 2006;Dag et al., 2008). However, achieving this fine balance between water use and crop yield requires both a comprehensive knowledge of crop response to water stress and an optimized irrigation schedule (Geerts & Raes, 2009).
Optimization of irrigation relies on identification of real time crop conditions and its sensitivity to water stress, both being reflections of specific physiological status, soil moisture, and climatic conditions (Centritto et al., 2000;Tognetti et al., 2009). It is, therefore, beneficial to exploit monitoring tools that provide accurate information regarding crop water status.
Crop water status may be determined by soil-based measurements, direct sensing of plant water status parameters, or indirect sensing of plant response to stress. Soil-based assessments include point measurements of water content and/or water potential; these are limited due to the difficulty and expense of satisfactorily representing the heterogenic conditions found in the root zone (Campbell & Campbell, 1982;Charlesworth, 2005). Plant water stress may be measured in terms of stem water potential; stomatal conductance; sap-flow; or as changes in leaf, stem, or trunk size; canopy water content (CWC); and relative water content (RWC). While all of these techniques are capable of providing accurate reports of actual crop water stress, they are destructive, labor intensive, localized, limited by small sample size, and unsuitable for automation (Ballester et al., 2013;Berni et al., 2009;Cohen et al., 2005;Gontia & Tiwari, 2008;Jones, 1999;Leinonen & Jones, 2004). It is, therefore, instructive to develop reliable, fast, simple, practical, and economical high-throughput sensing methods for the assessment of water stress in crops (Elsayed et al., 2017).
Spectral reflectance techniques have been demonstrated to be instantaneous, cheap, and non-destructive alternative methods for integrative large-scale assessment of several phenotypic parameters under different environmental conditions Elsayed et al., 2017;Gitelson et al., 2003;Lobos et al., 2014;Rapaport et al., 2017;Serrano et al., 2010;Sun et al., 2008). An additional advantage of reflectance indices is that they can be used for assessing plant water status since they change in response to crop water content (Penuelas et al., 1997;Stimson et al., 2005;Ustin et al., 1998). For instance, drought stress influences spectral reflectance values by causing alterations in leaf cell structure and composition via such changes in the relationships between cell walls and air spaces, cell sizes and shapes, and/or cell wall composition and structure (Grant, 1987;Penuelas et al., 1994).
Previous study has shown that a number of spectral indices might be used to estimate plant water status (El-Shikha et al., 2007;Gutierrez et al., 2010;Schmidhalter et al., 2001;Yang et al., 2016).
Although a large number of indices at diverse wavelengths have been proposed based on theoretical considerations for assessing plant water status, they have not been adequately validated against field data (Serrano et al., 2000;Sims & Gamon, 2003). It is the objective of the present study to determine the performance of select spectral indices in assessing such water status related parameters in olive cultivars as RWC and soil water content (SWC) under different irrigation regimes in the arid region of Ahvaz, Iran. Ahvaz International Airport where maximum air temperature might exceed 50 C during the hottest months of July and August but rarely ever drops below 0 C during the cold months of December and January. Most rainfall events in the region occur from November to February, with dry and hot spring and summer months.
The orchard used in this study housed mature (17-year-old) olive (O. europaea L.) trees of the two Koroneiki and T 2 cultivars planted at spaces of 5 Â 6 m. The trees were irrigated every 3 days using a drip irrigation system with a nominal factory discharge of 75 L h À1 , 1.5-7 bar, Pressure Compensating (Iran Drip, Iran) supplying water through a pipeline running between the tree rows and one bubbler for each tree. The experiments were conducted using four irrigation regimes, namely, IR 1 , irrigated up to 100% estimated crop evapotranspiration (ETc); IR 2 , irrigated up to 85% ETc; IR 3 , irrigated up to 70% ETc; and IR4, irrigated up to 55% ETc.
The split plot was used as the experimental design arranged based on the Randomized Complete Block Design (RCBD) with four replications. The two olive cultivars were designated as the main factors and the four irrigation regimes as the sub-main factors. A total of 32 trees were used in this study (16 trees of each cultivar). Such that four trees (one tree per block) of each cultivar per irrigation treatment could be measured ( Figure 1).
The upper 1.0 m of the soil profile at the experimental site is characterized by a sandy clay loam texture containing 52% sand, 24% silt, and 23% clay with an electrical conductivity of 5.6 dS m À1 , an organic content of 1.7%, a bulk density of 1.43 g cm À3 , a total N of .5%, an Olsen-P value of 30.7 mg kg À1 , and a pH of 7.38. The SWC at field capacity (À.3 MPa) and at wilting point (À1.5 MPa) were determined to be .21 and .09, respectively.

| Irrigation water demand
Crop water requirement (ETc) for the full irrigation regime (1.00 ETc) was determined using the following equation: The reference daily evapotranspiration (ETo) as calculated by the Food and Agriculture Organization (FAO) according to the modified Penman-Monteith method (Allen et al., 1998) is expressed by Equation (2): where Eto is the reference evapotranspiration (mm day À1 ), Rn is the net radiation at crop surface (MJ m À2 day À1 ), G is the soil heat flux F I G U R E 1 Experimental design layout of the field experiment in a split plot arrangement in a Randomized Complete Block Design (RCBD). IR 1 , irrigated up to 100% estimated crop evapotranspiration (ETc); IR 2 , irrigated up to 85% ETc; IR 3 , irrigated up to 70% ETc; and IR 4 , irrigated up to 55% ETc. C 1 , cultivar 1 (Koroneiki); C 2 , cultivar 2 (T 2 ). density (MJ m À2 day À1 ), T is the air temperature at 2-m height ( C), u 2 is the wind speed at 2-m height (m s À1 ), es is the saturation vapor pressure (kPa), ea is the actual vapor pressure (kPa), es À ea is the saturation vapor pressure deficit (kPa), Δ is the slope of vapor pressure curve (kPa C À1 ), and γ is the psychrometric constant (kPa C À1 ).
The values of crop coefficient (Kc) for olive tree suggested by FAO-56 were adjusted to the conditions prevalent in the study area.
The total amount of irrigation water applied during the study period for 1.00 ETc was 651 mm, which was subsequently reduced to 554, 456, and 328 mm in the 85%, 70%, and .55% ETc treatments, respectively.

| Leaf sampling
The leaf sampling was accomplished on three different days on July 3, October 29, and December 16, 2018, at midday between 11.00 and 14.00 h under cloudless conditions. In overall in every single sampling, eight fully developed Sun-exposed leaves, which were located in the middle of canopy were excised from each tree (five leaves were used for RWC measurements, and three more leaves were used for spectral measurements). The leaf samples were then sealed in plastic bags and kept at temperatures below 5 C before they were transferred to the laboratory.

| Relative water and soil water measurements
RWC was used to describe the water status of the two olive cultivars under the four irrigation regimes. RWC was determined in accordance with the procedure set out by Turner (1981). In this procedure, leaf samples were weighed immediately after its excision, to obtain leaf fresh weight (FW) before they were rehydrated in de-ionized water for approximately 24 h at 25 C until they grew fully turgid. Following rehydration, each leaf was blotted and immediately weighed to obtain their turgid weight (TW). Subsequently, the samples were dried in an oven at 70 C to obtain constant leaf dry weight (DW). Finally, RWC was determined as the average of the values measured in five fully expanded leaves from each tree using the following equation.
Soil water gravimetric measurements were performed simultaneously with the RWC measurements using samples taken at a depth of 20 cm and an average distance of 50 cm from the tree trunk.

| Spectral reflectance measurements
Concurrent with other measurements, spectral reflectance was measured using a portable spectrometer (ASD FieldSpec 3, Analytical Spectral Devices Inc, USA) operated in the spectral range of 350-2,500 nm with an average spectral resolution of 3 nm (Full-Width-Half-Maximum) and a sampling interval of 1.4 nm at the leaf level.
The handheld FieldSpec 3 sensor consisted of two units: one linked to a diffuser to measure light radiation as a reference signal and a fiber optic unit measuring canopy reflectance (Elsayed et al., 2017;Rischbeck et al., 2016).
Prior to each spectral measurement, the device was calibrated using a white barium sulfate (BaSO4) plate to provide maximum reflectance (Labsphere Inc, North Sutton, USA) (Knighton & Bugbee, 2004).
To reduce errors associated with illumination effects, a fiber optic contact probe was pressed on the leaf surface so that it would be illuminated only by the constant light source placed inside the contact probe (Contact Probe, Analytical Spectral Devices, Boulder, CO). To minimize differences in background reflectance due to electromagnetic radiation transmitted through the leaf, a spectrally black surface was placed on the underside of the leaf. Finally, the average of spectral measurements at the level of three leaves was considered as the spectrum for each tree.
The advantage of taking spectra at the leaf level is that the likely relationships between plant water status and spectral indices are not affected by background variables or atmospheric noise. It can, thus, be assumed that variations in spectral indices solely reflect changes in leaf properties. However, it is not just plant water status that might lead to variations in leaf properties; hence, no straightforward relationship can be directly established between plant water status and spectral indices.

| Variations of RWC, SWC, and spectral indices between irrigation regime and cultivar
The different irrigation regimes used in this study led to varying degrees of plant water stress. The differences in effects of irrigation  Regardless of the irrigation regime employed, no significant differences were observed between Koroneiki and T2 with respect to RWC, SWC, or spectral indices, neither did the interaction between irrigation regime and cultivar have any significant effects on these parameters. Compared with the control, however, these cultivars exhibited significant decreases in RWC, SWC, and WI but significant increases in their normalized spectral indices. Moreover, the leaf level WI and NWI1-5 values that are extensively used for RWC (Gutierrez et al., 2010;Penuelas et al., 1993;Serrano et al., 2000) and SWC (Gutierrez et al., 2010) assessments showed significant changes in plants subjected to drought stress (Table 1).

Spectral indices were strongly affected by irrigation regimes in
July and November and when averaged over the three measurements, while no significant differences in spectral indices were observed between irrigation treatments in December although the five normalized spectral indices and WI were slightly higher in the deficit plants and the control, respectively. Indeed, these five normalized spectral indices increased in the two olive cultivars studied while WI decreased with increasing water stress. For example, as reported in Table 1, the mean values obtained from the three measurements ranged from À.0194 to À.0174 for the normalized water stress index-4 (R970 À R880)/(R970 + R880) and from 1.0425 to 1.0366 for the WI (R970/R900).

| Relationships of spectral indices with RWC and SWC
The selected spectral indices were found in closely related to RWC and SWC not only for each cultivar and measurement but across the two olive cultivars as well ( Generally, the normalized spectral water index of NWI-2 (i.e., (R970 À R850)/(R970 + R850)) showed the least relationship with RWC and SWC across the measurements (lower in the range 1-35% when compared with the other spectral indices). The four spectral indices of WI, NWI-1, NWI-4, and NWI-5 sometimes demonstrated similar results without significant differences among them.
Moreover, these four spectral indices were found more accurate than the two NWI-3 and NWI-2 in predicting the RWC and SWC in olive trees.
A growing body of studies shows significant correlations between different water related parameters and spectral indices at different wavelengths, suggesting non-destructive and expeditious spectroradiometric methods of plant water status assessment (El-Hendawy, Elsayed et al., 2017;Erdle et al., 2011Erdle et al., , 2013. This is line with the report by Serrano et al. (2000), who showed that (2017) showed that the different water related parameters (i.e., RWC and CWC), and grain yield (GY) in wheat cultivars were significantly correlated with NWI-1, NWI-3, NWI-4, NWI-5, and NWI-990-992.
T A B L E 2 Coefficients of determination (R 2 ), slope (a), and intercept (b) of the relationships established for spectral indices with RWC and SWC in Koroneiki, T2, and both cultivars subjected to the four irrigation regimes in the first, second, and third rounds of measurements. F I G U R E 3 Relationships between spectral indices (WI and NWI1-5) and RWC in Kroneiki ( • ), T 2 ( ∎ ), and both cultivars (Â) subjected to the four irrigation regimes in the three measurements during the growing season with linear regressions fitted. *Statistically significant at p ≤ .05 and **statistically significant at p ≤ .01, respectively.
However, the present results are in contrast with those reported by Sun et al. (2008), who estimated WI on detached leaves of waterstressed olive saplings grown in pots. It is suggested that experimental design and measurement methodology might account for the conflicting results observed.
Pooling together the values collected from all the measurements led to the establishment of a significantly linear relationship (R 2 = .51 to .77, p < .01) between RWC and the five normalized spectral indices (viz., NWI-1, NWI-2, NWI-3, NWI-4, and NWI-5) alongside WI ( Figure 3). Furthermore, the spectral indices also scaled linearly with F I G U R E 4 Relationships between spectral indices (WI and NWI1-5) and SWC in Kroneiki ( • ), T 2 ( ∎ ), and both cultivars (Â) subjected to the four irrigation regimes in the three measurements during the growing season with linear regressions fitted. *Statistically significant at p ≤ .05 and **statistically significant at p ≤ .01, respectively. SWC (Figure 4) albeit these latter relationships were weaker than the former (i.e., R 2 = .45 to .67, p < .01).

| RWC and SWC modeling
Regression curves were fitted to develop empirical models based on the linear relationships derived and using a dataset of 64 data from the first and second measurements (67% of each measured RWC and SWC) used as training data with the remaining 32 data from the third measurement (33%) retained as test data. The statistical parameters included coefficients of determination (R 2 ) and RMSE. Furthermore, the intercept and slope of the linear regressions between the observed and predicted values were calculated as a measure of the difference between observed and predicted values for RWC and SWC.
Comparisons of the parameters estimated using the empirical models and the actual measured data are presented in Figures 5 and 6 that simultaneously shows the various statistical parameters used for the validation of the models and determining their accuracy based on differences between the observed and predicted RWC and SWC values on a 1:1 scatter plot line. A model with the highest values of R 2 and slope but the lowest values of RMSE was identified as one with the highest predictive accuracy. The models that fulfilled most of the validation criteria based on the four statistical parameters were selected for accurate prediction of RWC ( Figure 5) and SWC F I G U R E 5 Scatter plots containing root-mean-square error (RMSE), coefficients of determination (R 2 ), and functions of linear relationships between observed and predicted values of relative water content based on the data of six spectral indices plotted on 1:1 line. The calibration functions were validated using independent data obtained from the third set of measurements.
( Figure 6). Based on these criteria, the different models of the six spectral indices yielded more accurate estimates of RWC than they did of SWC. The results also revealed that the spectral indices studied were effective in estimating relative values of both olive leaf RWC and SWC as indicators of plant water stress. Among the six spectral indices, four satisfied most of the criteria used to determine the accuracy of the models in predicting RWC and SWC (Figures 5 and 6). It is clear that the models based on WI, NWI-1, NWI-4, and NWI-5 provided more accurate estimates of RWC and SWC albeit the estimated values of the parameters studied were always less than the measured ones over the entire set of measurements. These results confirmed the ability of spectral indices for non-destructive and rapid determination of RWC in olive leaves and SWC that provide a quantitative measure of the water status of the olive tree in the field, feasibility of optimal and precise management of irrigation, something that will have great importance to olive cultivation in arid countries such Iran.

| CONCLUSION
Olive is becoming a strategic crop worldwide, and the olive orchards are being increasingly irrigated to enhance its production. To save irrigation water and increase its productivity, there is a pressing need for monitoring plant water status to detect signs and degrees of prevalent water stress. It was hypothesized that the RWC levels of olive cultivars and the SWCs of cultivation sites could be reliably determined using spectral indices under different irrigation regimes. The present study was, hence, conducted on mature olive plantations in an arid environment. The findings indicated that (a) RWC, SWC, and spectral indices of WI and NWI 1-5 estimated at the leaf level were significantly affected by irrigation regimes; (b) the above six indices showed strong linear associations with RWC and SWC; (c) WI, NWI-1, NWI-4, and NWI-5 outperformed NWI-3 and NWI-2 in predicting RWC and SWC levels as plant water status indices in olive trees; and (d) spectral F I G U R E 6 Scatter plots containing root-mean-square error (RMSE), coefficients of determination (R 2 ), and functions of linear relationships between observed and predicted values of soil water content (SWC) based on the data of six spectral indices plotted on 1:1 line. The calibration functions were validated using independent data obtained from the third set of measurements.
indices yielded more accurate estimates of RWC than they did of